System digital twin for sustainable olive mill waste upcycling

OLinWASTE is our recently funded €4 million EU Horizon project, involving 10 consortium partners from 6 European countries. The project aims to revolutionise the biorefinery sector by pioneering an integrated zero-emission system and setting a new benchmark for sustainable innovation. This project will upcycle underutilised olive mill waste (OMW) into high-value products, including plant bioimmunostimulants, biopesticides, biofertilisers, biobased plastics, and bioenergy. Key focuses include reducing greenhouse gas emissions, optimising exhaust flows, minimising noise, recovering thermal energy, and reducing odours from pomace and digestate.

The project comprises seven work packages. As a leading partner, the University of Surrey will develop a system digital twin (SDT) to enhance overall sustainability, efficiency, and the optimisation of material, energy, and waste flows. The main tasks include: 1. Developing Digital Twins: Using data from other work packages, we will create digital twins (DTs) for waste treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations to develop an SDT that simulates real-time system operation in a dynamic virtual environment. 3. Online Implementation: Deploying the SDT on an evolving online platform, continuously updated with new data and novel multi-objective optimisation algorithms, to evaluate metrics such as material circularity, system efficiency, cost, and carbon footprint.

The University of Surrey is ranked 12^th in the UK in the 2025 Complete University Guide and was awarded The University of the Year for Graduate Employment in 2021. The Surrey Circular Economy group and the main supervisor Dr Lei Xing has built strong track record in circular chemical economy, green energy, carbon capture and utilisation, AI and digitalisation.

Open to any UK or international candidates.

By the start of the PhD programme, applicants must be awarded a master’s degree in any of the following disciplines: Chemical Engineering, Physics, Mechanical Engineering, Energy Engineering, Environmental Engineering, Computer Science, or other related disciplines. Applicants with track records or experience in system modelling, machine learning and control are highly desirable.